Defining and Developing a Generic Framework for Monitoring Data Quality in Clinical Research

Evidence for the need for high data quality in clinical research is well established. The rigor of clinical research conclusions rely heavily on good quality data, which relies on good documentation practices. Little attention has been given to clear guidelines and definitions to monitor data qualit...

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Bibliographic Details
Published in:AMIA ... Annual Symposium proceedings 2018, Vol.2018, p.1300-1309
Main Authors: Houston, Miss Lauren, Yu, A/Prof Ping, Martin, Dr Allison, Probst, Dr Yasmine
Format: Article
Language:English
Subjects:
Biomedical Research - standards
Clinical Trials as Topic - standards
Data Accuracy
Humans
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Summary:Evidence for the need for high data quality in clinical research is well established. The rigor of clinical research conclusions rely heavily on good quality data, which relies on good documentation practices. Little attention has been given to clear guidelines and definitions to monitor data quality. To address this, a "fit-for-use" data quality monitoring framework (DQMF) for clinical research was developed based on a holistic design-oriented approach. An integrated literature review and feasibility study underpinned the framework development. Ontology of key terms, concepts, methods, and standards were recorded using a consensus approach and mind mapping technique. The DQMF is presented as a nested concentric network illustrating concept relationships and hierarchy. Face validation was conducted, and common terminology and definitions are listed. The consolidated DQMF can be adapted according to study context and data availability aiding in the development of a long-term strategy with increased efficacy for clinical data quality monitoring.
ISSN:1559-4076